Diabetic nephropathy (DN) is the leading cause of chronic renal disease in the world. Though some modifiable risk factors have been identified, data from Caucasians and Pima Indians indicate that DN is genetically determined. The Wake Forest University (WFU) group has demonstrated that African Americans (AA) are at greater risk for DN compared to Caucasians. To identify DN susceptibility loci, the Case Western Reserve University (CWRU) and WFU groups are conducting NIH-funded projects and participating in the NIH-sponsored Family Investigation of Nephropathy and Diabetes (FIND) consortium. In contrast to these studies, which categorize subjects by discrete phenotypes, unraveling the genetic basis of complex diseases may be more easily accomplished through alternative strategies that include deconstructing phenotypes into continuous, quantitative traits. Building on our existing studies, we hypothesize hat the intermediate phenotypes, proteinuria and glomerular filtration rate (GFR) change, which respectively predict and measure progression of diabetic nephropathy to ESRD, are heritable. The hypothesis will be tested in a longitudinal study of AA and Caucasian families, which will permit the natural history of DN in AA to be defined for the first time. Data will be generated by quantitative trait locus (QTL) analysis, which offers advantages to the ongoing strategies, including enrollment and better definition of the clinical course of DN subjects with intermediate phenotypes (e.g. microalbuminuria), which have previously been excluded in our ongoing genetic analyses. Our specific aims are (1) AA and Caucasian families will be ascertained through index cases already enrolled in the FIND study with ESRD secondary to DN. Their diabetic siblings (n = 1200, about 60% AA) will be phenotyped and followed longitudinally. Dependent outcomes will include yearly urine albumin excretion and GFR measurements, (2) to assess association of genomic regions with the intermediate, quantitative phenotypes of proteinuria and GFR change, molecular and statistical methods will be used to examine regions suggestive of linkage with DN, which have been identified by the FIND genome scan. Cases and controls will be compared using both univariate and multivariate approaches. Environmental correlates will be included in the models.